First, we load and filter the count matrix.
Log transformation increases normal distribution of sample measurements for the respective metabolites
PCA
Next, we perform clustering and correlation analysis, and plot these results in a couple of heatmaps. Most of the samples looked fine, no sample was removed as outlier.
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Heatmap of distances
Heatmap of correlations
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Hierarchical clustering
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We performed a differential abundance analysis by parametric testing (two-sided t-test). The mean value of replicates per patient-derived cell line was used.
And these are the results of our differential abundance analysis. Only genes with a signficant adjusted p-value are shown.
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